Edge Computing Vs.Cloud Computing
With the increasing popularity and spread of the Internet of Things (IoT), more computing power and large amounts of data are generated at the periphery of computer networks. Previously, the data generated by the IoT devices was transmitted to a network server located in a central location. After the data is processed, additional tasks are sent back to those devices at the network edge.
This creates new problems in processing large amounts of data. First, the time it took to process the data sent from the terminal to the central server can cause delays. Second, sending large amounts of data across networks and devices can overload bandwidth. This process can accumulate latency, which would result in huge losses for the company.
Today, most of the existing IoT applications perform their computations in the cloud with large centralized servers. With edge computing, it shifts the entire process from data storage to computation in a decentralized location – closer to the end-user. It brings data storage and computing closer to the data source or to the edge of the network. It deals with implementing the application as close as possible to where the data is generated to eliminate time delays and thus save bandwidth.
Edge Computing is a substitute approach to computing and storing data in the cloud. Including the allocation of computing resources and services for applications via communication channels through a decentralized computing infrastructure. This puts resources closer to end-user devices rather than to centralized data centres located far on the network. It offers more processing of data closer to the source. This process minimizes data dependence on application services and speeds up the data processing procedure.
Advantages of Using Edge Computing
Apart from collecting data for transmission to the cloud, the collected data is also processed and analyzed locally by edge computing and necessary actions are performed on the collected data. Since this process is completed in milliseconds, it is important to optimize technical data independently of the process.
Transferring large amounts of real-time data inexpensively can be challenging, especially when done from remote industrial locations. This issue was addressed by adding intelligence to devices at the network edge. Edge computing brings analytical skills closer to the machine, eliminating the need for intermediaries. This setup offers a less expensive option for optimizing system performance.
Lower operating costs
In the cloud computing model, connectivity features, data migration, bandwidth, and latency are quite expensive. This inefficiency is eliminated by edge computing which has significantly fewer bandwidth requirements and less latency. By implementing edge computing, a valuable continuum from device to cloud is created that can process the large amount of data generated. Expensive bandwidth add-ons are no longer necessary, as no gigabytes of data will be transferred to the cloud. It also analyzes sensitive IoT data on private networks, thereby protecting sensitive data. Businesses today tend to prefer edge technology. This is due to optimized operational performance, addresses compliance and security protocols, as well as lower costs.
Edge computing can help reduce dependence on the cloud and thereby increase data processing speed. In addition, many modern IoT devices have computing and storage power. The transition to edge computing power allows these devices to be fully exploited.
Examples of Edge Computing
The use of this method is best demonstrated by a few examples of the use of edge computing. Here are a few scenarios where edge computing is most useful:
Self-driving or AI vehicles and other vehicles require large amounts of data from their environment in order to move properly in real-time. There would be a delay when using cloud computing.
Services like Netflix, Hulu, Amazon Prime and the upcoming Disney+ put a heavy burden on the network infrastructure. Edge computing contributes to a smoother experience by edge caching. In this case, popular content is cached in facilities closer to the end-user for easier and faster access.
As with streaming services, the growing popularity of smart homes is also a problem. The network load is now too large to rely solely on conventional cloud computing. Processing information closer to the source means less latency and faster response times in emergency scenarios. Examples are medical teams, firefighters or police operations.
Be aware that companies can lose control of their data when the cloud is in many places around the world. This setting can be used for multiple institutions, e.g., the problem, for example, is banks, which are legally required to only store data in their home country. While solutions are being worked on, cloud computing has clear drawbacks when it comes to data security in the cloud.
Cloud computing refers to the use of several services like software development platforms, storage, servers and other software via an Internet connection. Cloud computing providers have three common characteristics, which are mentioned below:
• Service is scalable
• Users must pay for the services used, which can include storage, processing time, and bandwidth.
• The cloud provider manages the application backend.
Benefits of Using Cloud Computing
Despite the many challenges that cloud computing faces, the cloud offers many advantages.
Cloud computing allows enterprises to start with small cloud deployments and grow relatively quickly and efficiently. Scaling can also be done quickly if the situation requires it. In addition, companies can add additional resources as needed to meet the growing needs of customers.
Services that use multiple redundant sites ensure business continuity and disaster recovery.
The cloud service provider maintains the system itself.
Cloud computing also supports mobile accessibility to a greater extent.
By using cloud computing, companies can significantly reduce their capital and operating costs while expanding their computing capacity.
EDGE COMPUTING VS CLOUD COMPUTING
Although edge and cloud computing are two different technologies, they are not interchangeable or replaceable in terms of their application. Processes included in edge computing are used for time-sensitive data, while cloud computing processes data that is not timed. Edge computing is preferred over cloud computing in remote locations that require local storage and when working with dedicated and intelligent devices.
Cloud computing collects data that is processed by smart devices before being sent to the cloud. This causes overloads in cloud data centres and in network systems. Therefore, cloud systems present challenges in terms of latency and data access inefficiency. Edge computing, on the other hand, helps analyze data close to the source. This not only reduces data dependence on application services but also speeds up data processing.
Cloud computing Vs. Edge Computing is not a direct debate or competitor. Instead, as a tandem, they offer more computing options for your enterprise needs. To implement this type of hybrid solution, identifying those requirements and comparing them to costs should be the first step in assessing what is best for you.